MIT6_851S10_lec05 - 6.851: Advanced Data Structures Spring...

Info iconThis preview shows pages 1–2. Sign up to view the full content.

View Full Document Right Arrow Icon

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full DocumentRight Arrow Icon
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: 6.851: Advanced Data Structures Spring 2010 Lecture 5 Feb 18 th , 2010 Prof. Erik Demaine and Dr. Andr e Schulz 1 Overview In the last lecture we started out by briey discussing range trees (a topic we started two lectures ago). In particular, we showed how to support dynamic insertions and deletions in range trees eciently. After this, we discussed the vertical line stabbing problem given a collection of intervals, support queries of the form How many intervals intersect the line x = x ?. We showed that both interval trees and segment trees could solve this problem eciently. Segment trees are a bit more memory-ecient, but interval trees are more exible for other applications. We used these data structures to solve the windowing problem given a collection of line segments, report how many lie in a given axis-aligned rectangle. In solving the windowing problem, we had to introduce an additional data structure called a priority search tree . In this lecture we study kinetic data structures. These are data structures that contain information about objects in motion. They support the following three types of queries: (i) modify the motion path of an object; (ii) move forward to a specified point in time; (iii) return information about the state of the objects in the current time. We will go over kinetic sorting and kinetic heaps, and then state several results about kinetic data structures without proof. After covering kinetic data structures, we will discuss the ray shooting problem given a simple (possibly non-convex) polygon, support queries asking for the first point of intersection of a ray with the polygon. We will show how to support O (log 2 ( n )) queries and outline an approach for achieving a query time of O (log( n )). 2 Kinetic Data Structures 2.1 Introduction A kinetic data structure is a data structure that stores moving data that is, data that changes in some predictable way over time. Examples would be the locations of physical objects over time. We could also look at, for example, trajectories of objects in R 2 and think of the x-coordinate as time. Kinetic data structures are useful, for example, in computer graphics and video games, to determine the next point in time at which two objects intersect. Data. Each data point has a value that is a known function of time. For example, we could have ane data ( x ( t ) = a + bt ). We could also have bounded-degree algebraic motion, or, more generally, pseudo-algebraic motion. Pseudo-algebraic means that all certificates of interest ip between true or false O (1) times as the objects move. It turns out that pseudo-algebraic will be the condition that we generally need in order for our kinetic data structures to run eciently....
View Full Document

Page1 / 10

MIT6_851S10_lec05 - 6.851: Advanced Data Structures Spring...

This preview shows document pages 1 - 2. Sign up to view the full document.

View Full Document Right Arrow Icon
Ask a homework question - tutors are online